Spatio-temporal graph learning has been widely applied to urban spatio-temporal forecasting tasks, analyzing the relationships between data to achieve accurate predictions. However, in recent years, model structures have become increasingly complex in pursuit of higher performance, resulting in greater computational co...
Wen-Chao Weng, Han-Yu Jiang, Mei Wu et al.· IEEE Transactions on Knowled...· 0 citations
Causal Abstraction Learning for Multi-Modal Grounded Planning (CALM) is proposed, a framework that enhances planning agents with the ability to discover and exploit causal regularities across tasks.
Xin-Shu Li, Shiyi Yang, Zi-Qi Xu et al.· Proceedings of the 32nd ACM...· 0 citations
Self-supervised Causal Effects Estimation is proposed, a novel framework that integrates causal priors with self-supervised learning to construct balanced and predictive representations for causal effects estimation that consistently outperforms state-of-the-art methods.
Xin-Shu Li, Shiyi Yang, Venus Haghighi et al.· ACM Transactions on Intellig...· 0 citations
Recent advances in multimodal embodied agents have enabled long-horizon planning in visually rich environments via natural language. Yet, their generalization remains brittle when task instructions deviate from familiar examples, exposing a reliance on surface imitation rather than structural understanding. We propose...
Xinshu Li, Shiyi Yang, Ziqi Xu et al.· Proceedings of the 32nd ACM...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
